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Artificial Intelligence: A New Synthesis (The Morgan Kaufmann Series in Artificial Intelligence) Hardcover – 17 Apr 1998

4.5 out of 5 stars 4 customer reviews

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Product details

  • Hardcover: 513 pages
  • Publisher: Morgan Kaufmann; International student edition edition (17 April 1998)
  • Language: English
  • ISBN-10: 1558604677
  • ISBN-13: 978-1558604674
  • Product Dimensions: 17.8 x 3 x 25.4 cm
  • Average Customer Review: 4.5 out of 5 stars  See all reviews (4 customer reviews)
  • Amazon Bestsellers Rank: 1,867,058 in Books (See Top 100 in Books)
  • See Complete Table of Contents

Product Description

About the Author

Nils J. Nilsson's long and rich research career has contributed much to AI. He has written many books, including the classic Principles of Artificial Intelligence. Dr. Nilsson is Kumagai Professor of Engineering, Emeritus, at Stanford University. He has served on the editorial boards of Artificial Intelligence and Machine Learning and as an Area Editor for the Journal of the Association for Computing Machinery. Former Chairman of the Department of Computer Science at Stanford, and former Director of the SRI Artificial Intelligence Center, he is also a past president and Fellow of the American Association for Artificial Intelligence.

Customer Reviews

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By A Customer on 2 Mar. 1999
Format: Hardcover
This is generally a good book with an effective unifying theme. However, each chapter is very dense, terse and presents complex material very quickly. This would not be my recommendation for a first book on AI. Author assumes the reader has a lot of preliminary informatin.
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Format: Hardcover
Although I do not recommend this book as a first one (because the material is too much dense for a begginer), it undoubtedly is a "must" in any set of books if you intend seriously working inside this area. Nilsson has a clear writing and (if you have some background) it's a pleasure to follow him even through hard topics.
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Format: Hardcover
This book covers a broad (almost comprehensive?) range of topics loosely aggregated under the heading "AI".
It is not intended for those wanting to research any particular AI discipline in detail, but provides a "leg-up" to those setting out in their studies of AI.
The review of neural networks (chapter 3) (of particular interest to me) is brief, covers salient maths for layered feedforward networks and backpropagation during supervised training, but does not cover recurrent networks or unsupervised learning (even for layered networks).
I would recommend this book for undergraduate students and those interested in a technical general treatment of AI.
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Format: Paperback Verified Purchase
good coverage of the field plus a lot of interesting extras, really recommended for Computer Science Students interested in the AI field .
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